An alternative methodology for mining seasonal pattern using self-organizing map

Vincent C.S. Lee

doi:10.1007/978-3-540-24775-3_52

An alternative methodology for mining seasonal pattern using self-organizing map

Denny, Vincent C.S. Lee

Department of Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

In retail industry, it is very important to understand seasonal sales pattern, because this knowledge can assist decision makers in managing inventory and formulating marketing strategies. Self-Organizing Map (SOM) is suitable for extracting and illustrating essential structures because SOM has unsupervised learning and topology preserving properties, and prominent visualization techniques. In this experiment, we propose a method for seasonal pattern analysis using Self-Organizing Map. Performance test with real-world data from stationery stores in Indonesia shows that the method is effective for seasonal pattern analysis. The results are used to formulate several marketing and inventory management strategies.

Original language	English
Title of host publication	Advances in Knowledge Discovery and Data Mining - 8th Pacific-Asia Conference, PAKDD 2004, Proceedings
Editors	Honghua Dai, Ramakrishnan Srikant, Chengqi Zhang
Publisher	Springer Verlag
Pages	424-430
Number of pages	7
ISBN (Print)	354022064X, 9783540220640
DOIs	https://doi.org/10.1007/978-3-540-24775-3_52
Publication status	Published - 2004
Event	8th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2004 - Sydney, Australia Duration: 26 May 2004 → 28 May 2004

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	3056
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	8th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2004
Country/Territory	Australia
City	Sydney
Period	26/05/04 → 28/05/04

Keywords

Clustering
Self-organizing maps
Temporal data
Visualization

Access to Document

10.1007/978-3-540-24775-3_52

Cite this

Denny, & Lee, V. C. S. (2004). An alternative methodology for mining seasonal pattern using self-organizing map. In H. Dai, R. Srikant, & C. Zhang (Eds.), Advances in Knowledge Discovery and Data Mining - 8th Pacific-Asia Conference, PAKDD 2004, Proceedings (pp. 424-430). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3056). Springer Verlag. https://doi.org/10.1007/978-3-540-24775-3_52

Denny ; Lee, Vincent C.S. / An alternative methodology for mining seasonal pattern using self-organizing map. Advances in Knowledge Discovery and Data Mining - 8th Pacific-Asia Conference, PAKDD 2004, Proceedings. editor / Honghua Dai ; Ramakrishnan Srikant ; Chengqi Zhang. Springer Verlag, 2004. pp. 424-430 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "An alternative methodology for mining seasonal pattern using self-organizing map",

abstract = "In retail industry, it is very important to understand seasonal sales pattern, because this knowledge can assist decision makers in managing inventory and formulating marketing strategies. Self-Organizing Map (SOM) is suitable for extracting and illustrating essential structures because SOM has unsupervised learning and topology preserving properties, and prominent visualization techniques. In this experiment, we propose a method for seasonal pattern analysis using Self-Organizing Map. Performance test with real-world data from stationery stores in Indonesia shows that the method is effective for seasonal pattern analysis. The results are used to formulate several marketing and inventory management strategies.",

keywords = "Clustering, Self-organizing maps, Temporal data, Visualization",

author = "Denny and Lee, {Vincent C.S.}",

note = "Publisher Copyright: {\textcopyright} Springer-Verlag Berlin Heidelberg 2004.; 8th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2004 ; Conference date: 26-05-2004 Through 28-05-2004",

year = "2004",

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series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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Denny & Lee, VCS 2004, An alternative methodology for mining seasonal pattern using self-organizing map. in H Dai, R Srikant & C Zhang (eds), Advances in Knowledge Discovery and Data Mining - 8th Pacific-Asia Conference, PAKDD 2004, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3056, Springer Verlag, pp. 424-430, 8th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2004, Sydney, Australia, 26/05/04. https://doi.org/10.1007/978-3-540-24775-3_52

An alternative methodology for mining seasonal pattern using self-organizing map. / Denny; Lee, Vincent C.S.
Advances in Knowledge Discovery and Data Mining - 8th Pacific-Asia Conference, PAKDD 2004, Proceedings. ed. / Honghua Dai; Ramakrishnan Srikant; Chengqi Zhang. Springer Verlag, 2004. p. 424-430 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3056).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - In retail industry, it is very important to understand seasonal sales pattern, because this knowledge can assist decision makers in managing inventory and formulating marketing strategies. Self-Organizing Map (SOM) is suitable for extracting and illustrating essential structures because SOM has unsupervised learning and topology preserving properties, and prominent visualization techniques. In this experiment, we propose a method for seasonal pattern analysis using Self-Organizing Map. Performance test with real-world data from stationery stores in Indonesia shows that the method is effective for seasonal pattern analysis. The results are used to formulate several marketing and inventory management strategies.

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Denny, Lee VCS. An alternative methodology for mining seasonal pattern using self-organizing map. In Dai H, Srikant R, Zhang C, editors, Advances in Knowledge Discovery and Data Mining - 8th Pacific-Asia Conference, PAKDD 2004, Proceedings. Springer Verlag. 2004. p. 424-430. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-540-24775-3_52

An alternative methodology for mining seasonal pattern using self-organizing map

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